Pedestrian detection has significantly progressed in recent years, thanks to the development of DNNs. However, detection performance at occluded scenes is still far from satisfactory, as occlusion increases the intra-class variance of pedestrians, hindering the model from finding an accurate classification boundary between pedestrians and background clutters. From the perspective of reducing intra-class variance, we propose to complete features for occluded regions so as to align the features of pedestrians across different occlusion patterns. An important premise for feature completion is to locate occluded regions. From our analysis, channel features of different pedestrian proposals only show high correlation values at visible parts and thus feature correlations can be used to model occlusion patterns. In order to narrow down the gap between completed features and real fully visible ones, we propose an adversarial learning method, which completes occluded features with a generator such that they can hardly be distinguished by the discriminator from real fully visible features. We report experimental results on the CityPersons, Caltech and CrowdHuman datasets. On CityPersons, we show significant improvements over five different baseline detectors, especially on the heavy occlusion subset. Furthermore, we show that our proposed method FeatComp++ achieves state-of-the-art results on all the above three datasets without relying on extra cues.

翻译：近年来，得益于深度神经网络的发展，行人检测取得了显著进展。然而，遮挡场景下的检测性能仍远未令人满意，因为遮挡增加了行人内部的类内方差，阻碍了模型在行人与背景杂波之间找到准确的分类边界。从降低类内方差的角度出发，我们提出对遮挡区域进行特征补全，以使不同遮挡模式下行人的特征对齐。特征补全的重要前提是定位遮挡区域。根据我们的分析，不同行人候选框的通道特征仅在可见部分呈现高相关性，因此特征相关性可用于建模遮挡模式。为缩小补全特征与真实全可见特征之间的差距，我们提出一种对抗学习方法，通过生成器补全遮挡特征，使其难以被判别器与真实全可见特征区分。我们在CityPersons、Caltech和CrowdHuman数据集上报告了实验结果。在CityPersons数据集上，我们展示了相对于五种不同基线检测器的显著改进，尤其是在严重遮挡子集上。此外，我们提出的FeatComp++方法无需依赖额外线索，即在上述三个数据集上均取得了最先进的结果。